New drug candidates are discovered by testing compounds against targets, a process termed screening. Traditionally, screening was a relatively slow process, with major pharmaceutical companies able to screen hundreds or a few thousands of compounds per week. This was acceptable, because the available compounds and biological targets were quite limited in number.
Recent advances in compound synthesis (e.g. combinatorial chemistry) and in the identification of biological targets (from genomics, proteomics and other disciplines) have led to a change in the nature of screening. There are many more compounds and the number of targets is also projected to grow rapidly. The extent of the growth can be appreciated if one considers that current drugs target about 450 of the estimated 50,000 potential gene products, each of which is a possible target. This is to say nothing of the targets that will be made available from the study of gene products (proteins). Therefore, the number of tests that could be done has become very large and will continue to grow. Pharmaceutical screening departments are implementing technologies which promise to increase the rate of testing. Their logic is that the more tests conducted per unit of time, the more often a new drug candidate will be discovered.
Screening at high rates is termed “high throughput screening” (HTS), and may be defined as the process of making thousands or many thousands of tests per day. HTS requires instruments and robotics optimized for high throughput, and systems for this purpose have been disclosed (e.g. US published patent application No. 2001/0028510 to Ramm et al.).
Most commonly, the instruments and robotics used for HTS do not accommodate tissues. Rather, they are applied to compounds and isolated targets. A compound of interest (referred to as the compound) is tested against a target (another compound, receptor molecule, protein or other), using label incorporation or some other property to reflect molecular interactions between the compound and its target. High throughput testing of compounds against targets is termed “primary screening.” Given that primary screening makes many thousands of tests per day, and that a proportion of those tests yields compounds worthy of further investigation (“hits”, usually less than 0.5% of the screen), hits generated by primary screening are accumulating at an unprecedented rate. These hits must be evaluated in post-primary screening stages, to characterize the efficacy, toxicity and specificity of the hit compounds. With these factors characterized, a small number of the best-qualified hits (“leads”) can be moved into very costly and time-consuming pre-clinical and clinical trials.
Unfortunately, post-primary testing is more complex and much slower than primary testing. It is not enough to simply detect molecular interactions between compounds and isolated target molecules. Rather, compounds must be tested for interaction with tissues. Therefore, the accumulation of hits is now a major bottleneck within the drug discovery pipeline and there is a need for post-primary tests which can verify leads at rates higher than possible in the past.
The bottleneck can be mitigated if post-primary tests are efficient in demonstrating interactions of compounds with biology. One promising path is to perform post-primary assays upon cells. Cells can provide a more biologically relevant test than is obtained from a simple compound mixture. At the same time, cell assays are less costly, much quicker to conduct and more socially acceptable than assays conducted in complex organisms (e.g. rodents). It is projected that the importance of cell-based assays will continue to grow, as cellular models for ogranismic response continue to develop and improve.
A potential problem with cell assays is the relatively low level of throughput that most evidence. For example, a “metabolic rate” method is disclosed by Dawes (1972), and a “pooled quantity” method described in Freshney (1987). These types of low throughput techniques are typical of those used to analyze cell populations without the use of imaging or other high throughput methods of detection.
To achieve higher rates of throughput, image-based measurements may be made upon cell populations (e.g. Malay et al., 1989; Schroeder and Neagle, 1996; Ramm, 1999), and may be combined with various methods for automating and optimizing the processes of handling, imaging, and analyzing the cellular samples. In these disclosures, the entity of measurement is a population of cells within each of a plurality of wells in a microwell plate. Cellular or subcellular detail is not resolved.
Detection of cell population responses may be contrasted with a requirement for detection of effects occurring within discrete cells in a population. In this case, cellular or subcellular resolution is required and a number of systems and methods for microscopic cell screening have been developed. As with population screens, the key is to construct systems and methods which automate and optimize the processes of handling, imaging, and analyzing the cellular samples. With the present invention, automated cell screens can be conducted with single cell and subcellular resolution.